PROJECT SUMMARY/ABSTRACT The proposed project aims to make healthcare safer through collection of patient-centered outcomes as the input data to support a safety and improvement model of the Learning Health System (LHS). The project will accomplish these aims by leveraging existing machine learning methods to classify free text documents, such as clinician notes, for the presence or absence of specific events of interest. The project shares this focus with two long-term objectives. The first broad project goal is to collect important data to address knowledge gaps in the incidence and clinical epidemiology of 5 serious pediatric inpatient healthcare acquired conditions (HACs). These 5 HACs are: peripheral IV infiltrates, venous thromboembolisms (VTEs), pressure injuries, patient falls, and incidents involving harm to providers. The second goal is to evaluate a novel approach to routine patient safety event surveillance that is scalable, transferrable, adaptable to other conditions and settings, and with low cost of sustainable ongoing operation. The project has two specific aims to achieve these goals: Aim 1: Implement enhanced surveillance for 5 pediatric HACs. Compare characteristics of previously and newly identified cases. Describe high-risk populations. Aim 2: Estimate completeness of existing systems. Evaluate effects of enhanced surveillance on quality improvement activities; incidence of HACs; and cost to operate system, including staff time and resources. The project team has developed a machine learning interface implemented in open license Windows software. The team has a lengthy track record making these methods accessible to clinicians and lay users in research, clinical operations, quality improvement, and injury prevention settings. The current project proposes an innovative application of these technologies, methods, and tools to the important problem of patient safety surveillance. An expected outcome of this project will be substantial advance in knowledge for each of the 5 pediatric HACs proposed for enhanced surveillance. Results will be reported in terms of existing data completeness and clinical epidemiology. Findings will directly address concerns over limitations of existing data sources and thereby drive patient safety improvement activities. An additional expected outcome will be the rigorous evaluation of a novel approach to patient safety surveillance. This will include analysis of the costs and benefits of enhanced surveillance with machine learning versus current approaches, and the cost-effectiveness of the approach compared to reliance on existing data, and external validation at a partner community hospital.